python对NGSIM数据集进行logistic建模的代码

下面是使用Python对NGSIM数据集进行Logistic建模的示例代码：

首先，我们需要导入所需要的库和数据集，这里使用pandas库来读取数据集：

import pandas as pd

# 读取数据集
data = pd.read_csv('NGSIM数据集路径')

接下来，我们需要对数据集进行一些预处理，包括：

- 选择需要建模的特征
- 对特征进行标准化处理
- 将数据集分为训练集和测试集

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# 选择需要建模的特征
features = ['Vehicle Length', 'Vehicle Width', 'Vehicle Class', 'Vehicle Velocity']

# 对特征进行标准化处理
scaler = StandardScaler()
data[features] = scaler.fit_transform(data[features])

# 将数据集分为训练集和测试集
train_data, test_data, train_labels, test_labels = train_test_split(data[features], data['Crash'], test_size=0.2, random_state=42)

然后，我们可以使用LogisticRegression模型来训练我们的数据：

from sklearn.linear_model import LogisticRegression

# 训练LogisticRegression模型
lr = LogisticRegression()
lr.fit(train_data, train_labels)

最后，我们可以使用测试集来评估我们的模型：

from sklearn.metrics import accuracy_score

# 使用测试集评估模型
predictions = lr.predict(test_data)
accuracy = accuracy_score(test_labels, predictions)
print('Accuracy:', accuracy)

完整的代码如下：

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 读取数据集
data = pd.read_csv('NGSIM数据集路径')

# 选择需要建模的特征
features = ['Vehicle Length', 'Vehicle Width', 'Vehicle Class', 'Vehicle Velocity']

# 对特征进行标准化处理
scaler = StandardScaler()
data[features] = scaler.fit_transform(data[features])

# 将数据集分为训练集和测试集
train_data, test_data, train_labels, test_labels = train_test_split(data[features], data['Crash'], test_size=0.2, random_state=42)

# 训练LogisticRegression模型
lr = LogisticRegression()
lr.fit(train_data, train_labels)

# 使用测试集评估模型
predictions = lr.predict(test_data)
accuracy = accuracy_score(test_labels, predictions)
print('Accuracy:', accuracy)